Machine Learning Infrastructure Engineer

About Zensors

Responsibility

• 
  
• 
  Optimizing Core ML Pipelines: Identifying key bottlenecks in our current video analytics pipeline and performing in-depth analysis to ensure the best possible performance on current server and edge compute architectures.
• 
  
• 
  Cross-Stack Collaboration: Collaborating closely with AI research and platform engineering teams to optimize core parallel algorithms and influence the design of our next-generation inference infrastructure.
• 
  
• 
  Model Acceleration: Applying advanced model optimization techniques—such as quantization (Int8/FP16), pruning, and layer fusion—to our Vision Transformers (ViTs) and CNNs to maximize throughput and minimize latency.
• 
  
• 
  Building Efficient Operators: Working across the entire ML framework/compiler stack (e.g., PyTorch, CUDA, TensorRT, and NVIDIA DeepStream) to write custom optimized ML operator libraries.
• 
  
• 
  Resource Efficiency: Reducing the compute cost per video stream to enable massive scalability of our SaaS product.
• 
  
• 
  Data Management: Building, improving, maintaining, and operating systems to facilitate the collection, labeling, and use of visual data for ML training.
• 
  

Requirements

• 
  
• BS/MS or Ph.D. in Computer Science, Electrical Engineering, or a related discipline.
• 
  
• Strong programming skills in C/C++ and Python.
• 
  
• Experience with model optimization, quantization, and efficient deep learning techniques (e.g., knowledge distillation, pruning).
• 
  
• Deep understanding of GPU hardware performance, including execution models, thread hierarchy, memory/cache management, and the cost/performance trade-offs of video processing.
• 
  
• Experience with profiling and benchmarking tools (e.g., Nsight Systems, Nsight Compute) to validate performance on complex architectures.
• 
  
• Experience identifying and resolving compute and data flow bottlenecks, particularly in high-bandwidth video processing pipelines.
• 
  
• Strong communication skills and the ability to work cross-functionally between research and infrastructure teams.
• 
  

Preferred Qualifications

• 
  
• Familiarity with database systems (e.g., SQL, Neo4j).
• 
  
• Work in Computer Vision, Deep Learning, and Vision Transformers.
• 
  
• Experience with video processing frameworks such as NVIDIA DeepStream, DALI, or FFmpeg.
• 
  
• Familiarity with ML compilers (e.g., TVM, MLIR) or inference engines like TensorRT or ONNX Runtime.
• 
  
• Knowledge of distributed training systems or cloud-scale inference serving (e.g., Triton Inference Server).
•